使用自然周边检测任务数据的驾驶员画像与贝叶斯工作负荷估计
Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data
AI总结：
本文提出了一种基于自然周边检测任务数据的驾驶员画像与贝叶斯工作负荷估计方法，通过监督学习和贝叶斯滤波实现近实时的工作负荷监测，F1分数分别达92%和81%。
摘要：
监测驾驶员的心理工作负荷有助于发起和维持与车载信息系统的安全交互，从而提供自适应的人机交互，减少对主要驾驶任务的影响。在本文中，我们解决了从驾驶性能数据中估计工作负荷的问题。首先，我们提出了一项新的道路研究，通过在自然环境中修改的周边检测任务来收集主观工作负荷数据。通过视频分析识别出引发高心理工作负荷的关键环境因素，例如交叉路口和前方车辆的行为。其次，引入了一个使用最先进的时间序列分类器（例如卷积神经网络和变换技术）的监督学习框架，以根据驾驶员在旅程中经历的平均工作负荷对驾驶员进行画像。然后提出了一种贝叶斯滤波方法，用于在（近）实时中顺序估计驾驶员的瞬时工作负荷。这种计算效率高且灵活的方法可以轻松地针对驾驶员进行个性化（例如，结合他们推断的平均工作负荷画像），适应驾驶/环境上下文（例如，道路类型），并扩展来自新来源的数据流。使用道路研究数据证明了所提出的画像和瞬时工作负荷估计方法的有效性，分别显示出高达92%和81%的F1分数。
英文摘要：
Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interaction with reduced impact on the primary task of driving. In this paper, we tackle the problem of workload estimation from driving performance data. First, we present a novel on-road study for collecting subjective workload data via a modified peripheral detection task in naturalistic settings. Key environmental factors that induce a high mental workload are identified via video analysis, e.g. junctions and behaviour of vehicle in front. Second, a supervised learning framework using state-of-the-art time series classifiers (e.g. convolutional neural network and transform techniques) is introduced to profile drivers based on the average workload they experience during a journey. A Bayesian filtering approach is then proposed for sequentially estimating, in (near) real-time, the driver's instantaneous workload. This computationally efficient and flexible method can be easily personalised to a driver (e.g. incorporate their inferred average workload profile), adapted to driving/environmental contexts (e.g. road type) and extended with data streams from new sources. The efficacy of the presented profiling and instantaneous workload estimation approaches are demonstrated using the on-road study data, showing $F_{1}$ scores of up to 92% and 81%, respectively.
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